Effective Web Personalization from Web Logs Using Tree Structure

In the day today life the information in World Wide Web is increasing in an explosive way and simultaneously the usage of the Web is also growing in an increasing way and for each and every usage or accessing of the web pages it creates a separate log entry in the web log file and so the log file is also increased correspondingly, From the web log file we get some interesting information about the users previous access sequence. From that interested information it is possible to predict their future access sequence and also personalize the most interested pattern to the users. In this paper we create a model using Tree structure concept, it first scan the collected web log file from that it creates the tree for each and every user and the next step is to traverse the tree using Level-by-Level tree traversal and find out the interested pattern for each user and personalize it to the users in their future access. Using the Level-by-Level tree traversal results the model clusters the uniform interested pattern among the users and it counts the number of clusters and find out the maximum number of interested pattern in the website and also it count the number of interested pattern in each cluster from that the model find out most interested path/pattern of the website during that period of time. From the cluster result it personalized the information to users in their future access. The proposed model is very useful for understanding the behavior of the users, find out the interested object of the website and based upon the results it improving the web site design too, find out the maximum interested pattern of the website, find out the most interested path/pattern of the website. Finally we have done the experimental studies of our proposed model using web log data from a reputed website and prove the efficiency of the proposed model.